Peidi Xu

Peidi Xu

PhD fellow, PhD student, Assistant lecturer

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    Publication year:
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    1 - 4 out of 4Page size: 10
    1. 2023
    2. Published

      A hybrid approach to full-scale reconstruction of renal arterial network

      Xu, Peidi, von Holstein-Rathlou, Niels-Henrik, Søgaard, S. B., Gundlach, C., Sorensen, Charlotte Mehlin, Erleben, Kenny, Sosnovtseva, Olga & Darkner, Sune, 9 May 2023, In: Scientific Reports. 13, 1, 15 p., 7569.

      Research output: Contribution to journal › Journal article › Research › peer-review

    3. Published

      Deep-learning-based segmentation of individual tooth and bone with periodontal ligament interface details for simulation purposes

      Xu, Peidi, Gholamalizadeh, T., nsv780, nsv780, Darkner, Sune & Erleben, Kenny, 2023, In: IEEE Access. 11, p. 102460-102470

      Research output: Contribution to journal › Journal article › Research › peer-review

    4. Published

      Extremely Weakly-Supervised Blood Vessel Segmentation with Physiologically Based Synthesis and Domain Adaptation

      Xu, Peidi, Lee, B., Sosnovtseva, Olga, Sorensen, Charlotte Mehlin, Erleben, Kenny & Darkner, Sune, 2023, Medical Image Learning with Limited and Noisy Data - 2nd International Workshop, MILLanD 2023, Held in Conjunction with MICCAI 2023, Proceedings. Xue, Z., Antani, S., Zamzmi, G., Yang, F., Rajaraman, S., Liang, Z., Huang, S. X. & Linguraru, M. G. (eds.). Springer, p. 191-201 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 14307 LNCS).

      Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

    5. 2022
    6. Published

      Auto-segmentation of Hip Joints Using MultiPlanar UNet with Transfer Learning

      Xu, Peidi, nsv780, nsv780, Gholamalizadeh, T., Nielsen, Michael Bachmann, Erleben, Kenny & Darkner, Sune, 2022, Medical Image Learning with Limited and Noisy Data: First International Workshop, MILLanD 2022 Held in Conjunction with MICCAI 2022 Singapore, September 22, 2022 Proceedings. Zamzmi, G., Antani, S., Rajaraman, S., Xue, Z., Bagci, U. & Linguraru, M. G. (eds.). Springer Science and Business Media Deutschland GmbH, p. 153-162 10 p. (Medical Image Learning with Limited and Noisy Data, Vol. 13559).

      Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

    ID: 226561698